Red-eye detection and correction technologies are used in printers, digital cameras, photo viewers, and image editing software to localize and correct the red-eye effects in digital photographs captured using a flash. Though there has been a great deal of progress in red-eye detection and correction in the last few years, many problems remain unsolved. For example, red-eye detection and correction must deal with varying illumination, low image quality and resolution, eye size and face orientation variations, and background changes in complex real-life scenes.
Typically, early stages of a red-eye detection pipeline have to distinguish between true red-eye objects and a number of incorrectly detected non-red-eye objects, also known as false red-eye objects. False red-eye objects are particularly prevalent in complex visual scenes. False red-eye objects can be reduced based on the evaluation of the objects' color, structural and geometric characteristics. Unfortunately, many real-world patterns exhibit similar color and structural characteristics as true red-eye objects, thus resulting in a high number of false red-eye objects even at higher stages of the detection pipeline.